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This paper deals with clustering of segments of stock prices by using nonlinear modeling system for time series based on the genetic programming (GP). We apply the GP procedure in learning phase of the system where we improve the nonlinear functional forms to approximate the models used to generate time series. The variation of the individuals with relatively high capability in the pool can cope with...
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